Dataset Information

Predicting Voltammetry Using Physics-Informed Neural Networks.

ABSTRACT: We propose a discretization-free approach to simulation of cyclic voltammetry using Physics-Informed Neural Networks (PINNs) by constraining a feed-forward neutral network with the diffusion equation and electrochemically consistent boundary conditions. Using PINNs, we first predict one-dimensional voltammetry at a disc electrode with semi-infinite or thin layer boundary conditions. The voltammograms agree quantitatively with those obtained independently using the finite difference method and/or previously reported analytical expressions. Further, we predict the voltammetry at a microband electrode, solving the two-dimensional diffusion equation, obtaining results in close agreement with the literature. Last, we apply a PINN to voltammetry at the edges of a square electrode, quantifying the nonuniform current distribution near the corner of electrode. In general, we noticed the relative ease of developing PINNs for the solution of, in particular, the higher dimensional problem, and recommend PINNs as a potentially faster and easier alternative to existing approaches for voltammetric problems.

SUBMITTER: Chen H

PROVIDER: S-EPMC9084599 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Publications

Predicting Voltammetry Using Physics-Informed Neural Networks.

Chen Haotian H Kätelhön Enno E Compton Richard G RG

The journal of physical chemistry letters 20220110 2

We propose a discretization-free approach to simulation of cyclic voltammetry using Physics-Informed Neural Networks (PINNs) by constraining a feed-forward neutral network with the diffusion equation and electrochemically consistent boundary conditions. Using PINNs, we first predict one-dimensional voltammetry at a disc electrode with semi-infinite or thin layer boundary conditions. The voltammograms agree quantitatively with those obtained independently using the finite difference method and/or ...[more]

PMID: 35007069

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Project description:Elasticity imaging is a technique that discovers the spatial distribution of mechanical properties of tissue using deformation and force measurements under various loading conditions. Given the complexity of this discovery, most existing methods approximate only one material parameter while assuming homogeneous distributions for the others. We employ physics-informed neural networks (PINN) in linear elasticity problems to discover the space-dependent distribution of both elastic modulus (E) and Poisson's ratio (ν) simultaneously, using strain data, normal stress boundary conditions, and the governing physics. We validated our model on three examples. First, we experimentally loaded hydrogel samples with embedded stiff inclusions, representing tumorous tissue, and compared the approximations against ground truth determined through tensile tests. Next, using data from finite element simulation of a rectangular domain containing a stiff circular inclusion, the PINN model accurately localized the inclusion and estimated both E and ν. We observed that in a heterogeneous domain, assuming a homogeneous ν distribution increases estimation error for stiffness as well as the area of the stiff inclusion, which could have clinical importance when determining size and stiffness of tumorous tissue. Finally, our model accurately captured spatial distribution of mechanical properties and the tissue interfaces on data from another computational model, simulating uniaxial loading of a rectangular hydrogel sample containing a human brain slice with distinct gray matter and white matter regions and complex geometrical features. This elasticity imaging implementation has the potential to be used in clinical imaging scenarios to reliably discover the spatial distribution of mechanical parameters and identify material interfaces such as tumors. STATEMENT OF SIGNIFICANCE: Our work is the first implementation of physics-informed neural networks to reconstruct both material parameters - Young's modulus and Poisson's ratio - and stress distributions for isotropic linear elastic materials by having deformation and force measurements. We comprehensively validate our model using experimental measurements and synthetic data generated using finite element modeling. Our method can be implemented in clinical elasticity imaging scenarios to improve diagnosis of tumors and for mechanical characterization of biomaterials and biological tissues in a minimally invasive manner.

Dataset Information

Predicting Voltammetry Using Physics-Informed Neural Networks.

Publications

Predicting Voltammetry Using Physics-Informed Neural Networks.

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